TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916256887603200 |
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| author | Pelaez, Raul P. Simeon, Guillem Galvelis, Raimondas Mirarchi, Antonio Eastman, Peter Doerr, Stefan Thölke, Philipp Markland, Thomas E. De Fabritiis, Gianni |
| author_facet | Pelaez, Raul P. Simeon, Guillem Galvelis, Raimondas Mirarchi, Antonio Eastman, Peter Doerr, Stefan Thölke, Philipp Markland, Thomas E. De Fabritiis, Gianni |
| contents | Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2-fold to 10-fold over previous iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and the smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_17660 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations Pelaez, Raul P. Simeon, Guillem Galvelis, Raimondas Mirarchi, Antonio Eastman, Peter Doerr, Stefan Thölke, Philipp Markland, Thomas E. De Fabritiis, Gianni Machine Learning Biological Physics Chemical Physics Computational Physics Achieving a balance between computational speed, prediction accuracy, and universal applicability in molecular simulations has been a persistent challenge. This paper presents substantial advancements in the TorchMD-Net software, a pivotal step forward in the shift from conventional force fields to neural network-based potentials. The evolution of TorchMD-Net into a more comprehensive and versatile framework is highlighted, incorporating cutting-edge architectures such as TensorNet. This transformation is achieved through a modular design approach, encouraging customized applications within the scientific community. The most notable enhancement is a significant improvement in computational efficiency, achieving a very remarkable acceleration in the computation of energy and forces for TensorNet models, with performance gains ranging from 2-fold to 10-fold over previous iterations. Other enhancements include highly optimized neighbor search algorithms that support periodic boundary conditions and the smooth integration with existing molecular dynamics frameworks. Additionally, the updated version introduces the capability to integrate physical priors, further enriching its application spectrum and utility in research. The software is available at https://github.com/torchmd/torchmd-net. |
| title | TorchMD-Net 2.0: Fast Neural Network Potentials for Molecular Simulations |
| topic | Machine Learning Biological Physics Chemical Physics Computational Physics |
| url | https://arxiv.org/abs/2402.17660 |